InvalidArgumentError:索引[3]=[02917]出现故障.许多稀疏操作需要排序索引

Question

我面临以下错误：：

InvalidArgumentError:indexs[3]=[02917]出现故障。许多稀疏操作需要排序索引。使用tf.sparse.reorder创建顺序正确的副本我不知道如何修复这个错误。我尝试了重新排序的方法，但不起作用

下面是代码：：

def score_transform(X):
y_reshaped = np.reshape(X['rating'].values, (-1, 1))
for index, val in enumerate(y_reshaped):
if val >= 8:
y_reshaped[index] = 1
elif val >= 5:
y_reshaped[index] = 2
else:
y_reshaped[index] = 0
y_result = to_categorical(y_reshaped)
return y_result

def convert_sparse_matrix_to_sparse_tensor(X):
coo = X.tocoo()
indices = np.mat([coo.row, coo.col]).transpose()
return tf.sparse.reorder(tf.SparseTensor(indices, coo.data, coo.shape))

df_train = pd.read_csv("/content/drive/MyDrive/NLP_Prj/drugsComTrain_raw.csv", parse_dates=["date"])
df_test = pd.read_csv("/content/drive/MyDrive/NLP_Prj/drugsComTest_raw 2.csv", parse_dates=["date"])
df_train, df_test = train_test_split(df_all, test_size=0.33, random_state=42) 
X_train=(df_train['review_clean'].to_numpy())
X_test=(df_test['review_clean'].to_numpy())
test_train = np.concatenate([X_train, X_test])
X_onehot = vectorizer.fit_transform(test_train)
X_onehot1=convert_sparse_matrix_to_sparse_tensor(X_onehot)
#Model
model = keras.models.Sequential()
model.add(keras.layers.Dense(200, input_dim=len(vectorizer.get_feature_names())))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(300))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(256, activation='relu'))
model.add(keras.layers.Dense(3, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

hist = model.fit(X_onehot1, y_train1, epochs=10, batch_size=128,verbose=1, validation_data=(X_onehot[157382:157482], y_train1[157382:157482]))

如何解决此问题？

Answer 1

发生此错误的主要原因是tensorflow和keras版本不匹配。不要像那样直接做导入tensorflow

import tensorflow as tf

当我使用下面的行时，错误被修复了。我可以训练这个模特。相反，使用以下代码行：

import tensorflow.python.keras.backend as K 
sess = K.get_session()